




import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


__a							     =					logging.get_logger(__name__)

__a							     =					{
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''instructblip_vision_model'''





    def __init__( self  ,_SCREAMING_SNAKE_CASE=1_408  ,_SCREAMING_SNAKE_CASE=6_144  ,_SCREAMING_SNAKE_CASE=39  ,_SCREAMING_SNAKE_CASE=16  ,_SCREAMING_SNAKE_CASE=224  ,_SCREAMING_SNAKE_CASE=14  ,_SCREAMING_SNAKE_CASE="gelu"  ,_SCREAMING_SNAKE_CASE=1e-6  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=1e-10  ,_SCREAMING_SNAKE_CASE=True  ,**_SCREAMING_SNAKE_CASE  ,)					->						Any:
         super().__init__(**_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Optional[int]  =     hidden_size
         UpperCAmelCase_     :				List[Any]  =     intermediate_size
         UpperCAmelCase_     :				Tuple  =     num_hidden_layers
         UpperCAmelCase_     :				Dict  =     num_attention_heads
         UpperCAmelCase_     :				Optional[Any]  =     patch_size
         UpperCAmelCase_     :				str  =     image_size
         UpperCAmelCase_     :				Optional[Any]  =     initializer_range
         UpperCAmelCase_     :				Optional[int]  =     attention_dropout
         UpperCAmelCase_     :				List[str]  =     layer_norm_eps
         UpperCAmelCase_     :				List[Any]  =     hidden_act
         UpperCAmelCase_     :				Any  =     qkv_bias





    @classmethod
    def 				a__ ( cls  ,_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )					->						"PretrainedConfig":
         cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_,       UpperCAmelCase_     :				Tuple  =     cls.get_config_dict(_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )

         # get the vision config dict if we are loading from InstructBlipConfig
         if config_dict.get('''model_type'''    ) == "instructblip":
              UpperCAmelCase_     :				Optional[Any]  =     config_dict['''vision_config''']

         if "model_type" in config_dict and hasattr(cls  ,'''model_type'''    ) and config_dict["model_type"] != cls.model_type:
              logger.warning(
                  f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
                  f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''    )

         return cls.from_dict(_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )


class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''instructblip_qformer'''





    def __init__( self  ,_SCREAMING_SNAKE_CASE=30_522  ,_SCREAMING_SNAKE_CASE=768  ,_SCREAMING_SNAKE_CASE=12  ,_SCREAMING_SNAKE_CASE=12  ,_SCREAMING_SNAKE_CASE=3_072  ,_SCREAMING_SNAKE_CASE="gelu"  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=512  ,_SCREAMING_SNAKE_CASE=0.02  ,_SCREAMING_SNAKE_CASE=1e-12  ,_SCREAMING_SNAKE_CASE=0  ,_SCREAMING_SNAKE_CASE="absolute"  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=1_408  ,**_SCREAMING_SNAKE_CASE  ,)					->						int:
         super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				List[Any]  =     vocab_size
         UpperCAmelCase_     :				Optional[int]  =     hidden_size
         UpperCAmelCase_     :				int  =     num_hidden_layers
         UpperCAmelCase_     :				Any  =     num_attention_heads
         UpperCAmelCase_     :				Optional[int]  =     hidden_act
         UpperCAmelCase_     :				List[Any]  =     intermediate_size
         UpperCAmelCase_     :				Optional[int]  =     hidden_dropout_prob
         UpperCAmelCase_     :				List[Any]  =     attention_probs_dropout_prob
         UpperCAmelCase_     :				List[Any]  =     max_position_embeddings
         UpperCAmelCase_     :				int  =     initializer_range
         UpperCAmelCase_     :				List[str]  =     layer_norm_eps
         UpperCAmelCase_     :				Tuple  =     position_embedding_type
         UpperCAmelCase_     :				int  =     cross_attention_frequency
         UpperCAmelCase_     :				Optional[int]  =     encoder_hidden_size





    @classmethod
    def 				a__ ( cls  ,_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )					->						"PretrainedConfig":
         cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_,       UpperCAmelCase_     :				Optional[int]  =     cls.get_config_dict(_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )

         # get the qformer config dict if we are loading from InstructBlipConfig
         if config_dict.get('''model_type'''    ) == "instructblip":
              UpperCAmelCase_     :				str  =     config_dict['''qformer_config''']

         if "model_type" in config_dict and hasattr(cls  ,'''model_type'''    ) and config_dict["model_type"] != cls.model_type:
              logger.warning(
                  f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
                  f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''    )

         return cls.from_dict(_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )


class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''instructblip'''
    lowerCAmelCase         = True





    def __init__( self  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=32  ,**_SCREAMING_SNAKE_CASE    )					->						List[str]:
         super().__init__(**_SCREAMING_SNAKE_CASE    )

         if vision_config is None:
              UpperCAmelCase_     :				List[Any]  =     {}
              logger.info('''vision_config is None. initializing the InstructBlipVisionConfig with default values.'''    )

         if qformer_config is None:
              UpperCAmelCase_     :				Tuple  =     {}
              logger.info('''qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'''    )

         if text_config is None:
              UpperCAmelCase_     :				str  =     {}
              logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).'''    )

         UpperCAmelCase_     :				List[str]  =     InstructBlipVisionConfig(**_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Optional[Any]  =     InstructBlipQFormerConfig(**_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Dict  =     text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
         UpperCAmelCase_     :				List[Any]  =     CONFIG_MAPPING[text_model_type](**_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Tuple  =     self.text_config.tie_word_embeddings
         UpperCAmelCase_     :				List[Any]  =     self.text_config.is_encoder_decoder

         UpperCAmelCase_     :				int  =     num_query_tokens
         UpperCAmelCase_     :				Any  =     self.vision_config.hidden_size
         UpperCAmelCase_     :				List[Any]  =     self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
         UpperCAmelCase_     :				Optional[Any]  =     1.0
         UpperCAmelCase_     :				Any  =     0.02





    @classmethod
    def 				a__ ( cls  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE  ,)					->						Optional[Any]:
         return cls(
             vision_config=vision_config.to_dict()  ,qformer_config=qformer_config.to_dict()  ,text_config=text_config.to_dict()  ,**_SCREAMING_SNAKE_CASE  ,)





    def 				a__ ( self    )					->						str:
         UpperCAmelCase_     :				Union[str, Any]  =     copy.deepcopy(self.__dict__    )
         UpperCAmelCase_     :				Any  =     self.vision_config.to_dict()
         UpperCAmelCase_     :				List[str]  =     self.qformer_config.to_dict()
         UpperCAmelCase_     :				Dict  =     self.text_config.to_dict()
         UpperCAmelCase_     :				Optional[Any]  =     self.__class__.model_type
         return output


import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
	    FlaxRobertaPreLayerNormForCausalLM,
	    FlaxRobertaPreLayerNormForMaskedLM,
	    FlaxRobertaPreLayerNormForMultipleChoice,
	    FlaxRobertaPreLayerNormForQuestionAnswering,
	    FlaxRobertaPreLayerNormForSequenceClassification,
	    FlaxRobertaPreLayerNormForTokenClassification,
	    FlaxRobertaPreLayerNormModel,
	)



class        A__(unittest.TestCase							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase  ,      _lowercase=13  ,      _lowercase=7  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=99  ,      _lowercase=32  ,      _lowercase=5  ,      _lowercase=4  ,      _lowercase=37  ,      _lowercase="gelu"  ,      _lowercase=0.1  ,      _lowercase=0.1  ,      _lowercase=512  ,      _lowercase=16  ,      _lowercase=2  ,      _lowercase=0.0_2  ,      _lowercase=4  ,      )     -> Tuple:
									a_						:						Tuple 							= parent
									a_						:						Optional[Any] 							= batch_size
									a_						:						int 							= seq_length
									a_						:						List[str] 							= is_training
									a_						:						str 							= use_attention_mask
									a_						:						Optional[int] 							= use_token_type_ids
									a_						:						Optional[int] 							= use_labels
									a_						:						int 							= vocab_size
									a_						:						Union[str, Any] 							= hidden_size
									a_						:						Optional[int] 							= num_hidden_layers
									a_						:						Optional[int] 							= num_attention_heads
									a_						:						Tuple 							= intermediate_size
									a_						:						int 							= hidden_act
									a_						:						Any 							= hidden_dropout_prob
									a_						:						int 							= attention_probs_dropout_prob
									a_						:						List[str] 							= max_position_embeddings
									a_						:						Optional[int] 							= type_vocab_size
									a_						:						List[str] 							= type_sequence_label_size
									a_						:						Optional[Any] 							= initializer_range
									a_						:						Optional[int] 							= num_choices





						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						int 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size    )

									a_						:						Dict 							= None
									if self.use_attention_mask:
												a_						:						List[str] 							= random_attention_mask([self.batch_size, self.seq_length]    )

									a_						:						List[str] 							= None
									if self.use_token_type_ids:
												a_						:						Dict 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.type_vocab_size    )

									a_						:						Any 							= RobertaPreLayerNormConfig(
									    vocab_size=self.vocab_size  ,      hidden_size=self.hidden_size  ,      num_hidden_layers=self.num_hidden_layers  ,      num_attention_heads=self.num_attention_heads  ,      intermediate_size=self.intermediate_size  ,      hidden_act=self.hidden_act  ,      hidden_dropout_prob=self.hidden_dropout_prob  ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,      max_position_embeddings=self.max_position_embeddings  ,      type_vocab_size=self.type_vocab_size  ,      is_decoder=_lowercase  ,      initializer_range=self.initializer_range  ,      )

									return config, input_ids, token_type_ids, attention_mask





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						Optional[Any] 							= self.prepare_config_and_inputs()
									a_							,					a_							,					a_							,					a_						:						Any 							= config_and_inputs
									a_						:						List[Any] 							= {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
									return config, inputs_dict





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						Optional[Any] 							= self.prepare_config_and_inputs()
									a_							,					a_							,					a_							,					a_						:						str 							= config_and_inputs

									a_						:						str 							= True
									a_						:						Any 							= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
									a_						:						Tuple 							= ids_tensor([self.batch_size, self.seq_length]  ,      vocab_size=2    )

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class        A__(a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				List[str]       					=			True

						_A    :				int       					=			(
						    (
						        FlaxRobertaPreLayerNormModel,
						        FlaxRobertaPreLayerNormForCausalLM,
						        FlaxRobertaPreLayerNormForMaskedLM,
						        FlaxRobertaPreLayerNormForSequenceClassification,
						        FlaxRobertaPreLayerNormForTokenClassification,
						        FlaxRobertaPreLayerNormForMultipleChoice,
						        FlaxRobertaPreLayerNormForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Optional[int] 							= FlaxRobertaPreLayerNormModelTester(self    )





						@slow
						def 			UpperCamelCase__					(    self    )     -> int:
									for model_class_name in self.all_model_classes:
												a_						:						Union[str, Any] 							= model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,      from_pt=_lowercase    )
												a_						:						Union[str, Any] 							= model(np.ones((1, 1)    )    )
												self.assertIsNotNone(_lowercase    )







@require_flax
class        A__(unittest.TestCase							):
						"""simple docstring"""






						@slow
						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Union[str, Any] 							= FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,      from_pt=_lowercase    )

									a_						:						Union[str, Any] 							= np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  ,      dtype=jnp.intaa    )
									a_						:						Tuple 							= model(_lowercase    )[0]
									a_						:						Tuple 							= [1, 11, 50_265]
									self.assertEqual(list(output.shape    )  ,      _lowercase    )
									# compare the actual values for a slice.
									a_						:						Tuple 							= np.array(
									    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]  ,      dtype=np.floataa    )
									self.assertTrue(np.allclose(output[:, :3, :3]  ,      _lowercase  ,      atol=1e-4    )    )





						@slow
						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						Union[str, Any] 							= FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,      from_pt=_lowercase    )

									a_						:						List[str] 							= np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  ,      dtype=jnp.intaa    )
									a_						:						str 							= model(_lowercase    )[0]
									# compare the actual values for a slice.
									a_						:						Union[str, Any] 							= np.array(
									    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]  ,      dtype=np.floataa    )
									self.assertTrue(np.allclose(output[:, :3, :3]  ,      _lowercase  ,      atol=1e-4    )    )


